Index
231
undiscovered, 15, 27, 58
resource assessment, 4–5, 19, 33, 39,
83–4, 118, 141, 143–4, 161, 174
basic input data for, 143–4
methods, selection of, 174–5
methods, statistical treatment of.
See Appendices
objectives, 4–5
PETRIMES (Petroleum Exploration
and Resource Evaluation
System) approach, 3–6, 14–18,
39–40, 51, 57, 73, 90–91, 108,
119–20, 124, 143, 149–51, 161,
166, 168, 175
assessment team, 136–9, 143, 145,
149
feedback mechanism of, 6, 17, 27,
140–41, 149–51, 174–5
as learning process, 174
probability distributions, 90–91
steps for updating (annual update
procedure), 149
Windows version
(PETRIMES/W), 4, 40, 43
procedure, 4–5, 11, 19, 43–4, 50–51,
61–3, 88–9, 91, 116–19, 149
using Monte Carlo
method, 118–19
resource evaluation, 3–8, 11–13,
17–18, 20, 25–6, 45, 47, 50, 61,
80–81, 88, 107, 116, 118, 140, 150,
152–3, 171–2, 174–5
adapting pool size calculation, 116
appreciation/depreciation effect
of booked reserves on, 80–81
basic unit (geological model
representing natural
population), 7–8, 11, 17–18, 47,
50, 53, 57, 111, 132, 150, 174
deriving density of rth largest
pool, 45–7
estimating exploration risk, 107
feedback levels, 150
information required, 3–4, 45, 153
input, 3–4, 140, 143, 151
key objective, 26
methods, 3, 17, 27–33, 72–3, 85, 88,
100, 118–9, 152–66, 163–4,
166–73
Arps and Roberts fi nite
population, 3, 17, 72–3, 85, 152,
161, 163–4, 166
basin classifi cation, 152–5, 158
Bickel, Nair and Wang’s
nonparametric fi nite
population, 3, 17, 100, 152,
163–4
burial and thermal
history, 158–61
Chen and Sinding–Larsen’s geoanchored fi nite population, 3,
17, 152, 161, 163, 166
creaming, 153, 169–70
fractal, 153, 161, 171–3
Kaufman’s anchored fi nite
population, 3, 17, 27–33, 88,
152, 161, 164–5
Long’s superpopulation, 153, 170
Monte Carlo, 118–9
petroleum system (geochemical
mass balance), 154–161, 168
regression, 171–2
USGS log-geometric, 153, 161–2,
166–8
volumetric yield using analogous
basin, 152–3
Zipf’s law, 153, 168, 171
procedure, 4–8, 11–12, 26, 45, 107,
140, 150–51
evolution of, 152
identifying geological
populations, 7–8, 11, 140
testing distributional
assumptions, 88–9
Ricinus–Meadowbrook isolated reef
play, 9
risk, 111–12, 114, 116, 130–31, 139, 147,
151
analysis, 107, 115, 140
exploration, 5, 37, 39, 106–7, 111,
114–16, 127, 130–31, 139, 147, 151
factor, 112, 114, 138
most unpredictable, 138
group (play-level factor), 108
as superpopulation parameter, 111
Rundle–Jumping Pound gas
play, 80–84, 99
Clearwater A, 82, 84
Quirk Creek Rundle A, 82, 84
Jumping Pound West, 82
sample/sampling, 11–14, 16, 21–2,
25, 27–8, 31, 36–7, 41–3, 45,
78, 88–9, 101, 123, 150, 156,
158
biased, 25–8, 34, 45, 88
classifi ed as outlier, 22, 59, 94
distribution, J-shaped, 101, 105
mean and variance, 15, 28
ordered, 31, 43, 163
231
undiscovered, 15, 27, 58
resource assessment, 4–5, 19, 33, 39,
83–4, 118, 141, 143–4, 161, 174
basic input data for, 143–4
methods, selection of, 174–5
methods, statistical treatment of.
See Appendices
objectives, 4–5
PETRIMES (Petroleum Exploration
and Resource Evaluation
System) approach, 3–6, 14–18,
39–40, 51, 57, 73, 90–91, 108,
119–20, 124, 143, 149–51, 161,
166, 168, 175
assessment team, 136–9, 143, 145,
149
feedback mechanism of, 6, 17, 27,
140–41, 149–51, 174–5
as learning process, 174
probability distributions, 90–91
steps for updating (annual update
procedure), 149
Windows version
(PETRIMES/W), 4, 40, 43
procedure, 4–5, 11, 19, 43–4, 50–51,
61–3, 88–9, 91, 116–19, 149
using Monte Carlo
method, 118–19
resource evaluation, 3–8, 11–13,
17–18, 20, 25–6, 45, 47, 50, 61,
80–81, 88, 107, 116, 118, 140, 150,
152–3, 171–2, 174–5
adapting pool size calculation, 116
appreciation/depreciation effect
of booked reserves on, 80–81
basic unit (geological model
representing natural
population), 7–8, 11, 17–18, 47,
50, 53, 57, 111, 132, 150, 174
deriving density of rth largest
pool, 45–7
estimating exploration risk, 107
feedback levels, 150
information required, 3–4, 45, 153
input, 3–4, 140, 143, 151
key objective, 26
methods, 3, 17, 27–33, 72–3, 85, 88,
100, 118–9, 152–66, 163–4,
166–73
Arps and Roberts fi nite
population, 3, 17, 72–3, 85, 152,
161, 163–4, 166
basin classifi cation, 152–5, 158
Bickel, Nair and Wang’s
nonparametric fi nite
population, 3, 17, 100, 152,
163–4
burial and thermal
history, 158–61
Chen and Sinding–Larsen’s geoanchored fi nite population, 3,
17, 152, 161, 163, 166
creaming, 153, 169–70
fractal, 153, 161, 171–3
Kaufman’s anchored fi nite
population, 3, 17, 27–33, 88,
152, 161, 164–5
Long’s superpopulation, 153, 170
Monte Carlo, 118–9
petroleum system (geochemical
mass balance), 154–161, 168
regression, 171–2
USGS log-geometric, 153, 161–2,
166–8
volumetric yield using analogous
basin, 152–3
Zipf’s law, 153, 168, 171
procedure, 4–8, 11–12, 26, 45, 107,
140, 150–51
evolution of, 152
identifying geological
populations, 7–8, 11, 140
testing distributional
assumptions, 88–9
Ricinus–Meadowbrook isolated reef
play, 9
risk, 111–12, 114, 116, 130–31, 139, 147,
151
analysis, 107, 115, 140
exploration, 5, 37, 39, 106–7, 111,
114–16, 127, 130–31, 139, 147, 151
factor, 112, 114, 138
most unpredictable, 138
group (play-level factor), 108
as superpopulation parameter, 111
Rundle–Jumping Pound gas
play, 80–84, 99
Clearwater A, 82, 84
Quirk Creek Rundle A, 82, 84
Jumping Pound West, 82
sample/sampling, 11–14, 16, 21–2,
25, 27–8, 31, 36–7, 41–3, 45,
78, 88–9, 101, 123, 150, 156,
158
biased, 25–8, 34, 45, 88
classifi ed as outlier, 22, 59, 94
distribution, J-shaped, 101, 105
mean and variance, 15, 28
ordered, 31, 43, 163